In [12]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
from sklearn.model_selection import train_test_split
from sklearn.linear_model import PassiveAggressiveRegressor
In [13]:
data=pd.read_csv('C:/Users/Rakesh/Datasets/Instagram.csv', encoding='latin1')
data.head()
Out[13]:
Impressions From Home From Hashtags From Explore From Other Saves Comments Shares Likes Profile Visits Follows Caption Hashtags
0 3920.0 2586.0 1028.0 619.0 56.0 98.0 9.0 5.0 162.0 35.0 2.0 Here are some of the most important data visua... #finance #money #business #investing #investme...
1 5394.0 2727.0 1838.0 1174.0 78.0 194.0 7.0 14.0 224.0 48.0 10.0 Here are some of the best data science project... #healthcare #health #covid #data #datascience ...
2 4021.0 2085.0 1188.0 0.0 533.0 41.0 11.0 1.0 131.0 62.0 12.0 Learn how to train a machine learning model an... #data #datascience #dataanalysis #dataanalytic...
3 4528.0 2700.0 621.0 932.0 73.0 172.0 10.0 7.0 213.0 23.0 8.0 Here’s how you can write a Python program to d... #python #pythonprogramming #pythonprojects #py...
4 2518.0 1704.0 255.0 279.0 37.0 96.0 5.0 4.0 123.0 8.0 0.0 Plotting annotations while visualizing your da... #datavisualization #datascience #data #dataana...
In [14]:
data.isnull().sum()
Out[14]:
Impressions       1
From Home         1
From Hashtags     1
From Explore      1
From Other        1
Saves             1
Comments          1
Shares            1
Likes             1
Profile Visits    1
Follows           1
Caption           1
Hashtags          1
dtype: int64
In [15]:
data=data.dropna()
In [16]:
data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 99 entries, 0 to 98
Data columns (total 13 columns):
 #   Column          Non-Null Count  Dtype  
---  ------          --------------  -----  
 0   Impressions     99 non-null     float64
 1   From Home       99 non-null     float64
 2   From Hashtags   99 non-null     float64
 3   From Explore    99 non-null     float64
 4   From Other      99 non-null     float64
 5   Saves           99 non-null     float64
 6   Comments        99 non-null     float64
 7   Shares          99 non-null     float64
 8   Likes           99 non-null     float64
 9   Profile Visits  99 non-null     float64
 10  Follows         99 non-null     float64
 11  Caption         99 non-null     object 
 12  Hashtags        99 non-null     object 
dtypes: float64(11), object(2)
memory usage: 10.8+ KB

Analyzing Instagram Reach¶

In [17]:
plt.figure(figsize=(10,8))
plt.style.use('fivethirtyeight')
plt.title('Distribution of Impressions From Home')
sns.distplot(data['From Home'])
plt.show()

C:\Users\Rakesh\Downloads\Anaconda\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
In [18]:
plt.figure(figsize=(10, 8))
plt.title("Distribution of Impressions From Hashtags")
sns.distplot(data['From Hashtags'])
plt.show()

C:\Users\Rakesh\Downloads\Anaconda\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
In [10]:
plt.figure(figsize=(10, 8))
plt.title("Distribution of Impressions From Hashtags")
sns.distplot(data['From Hashtags'])
plt.show()

C:\Users\Rakesh\Downloads\Anaconda\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
In [19]:
plt.figure(figsize=(10, 8))
plt.title("Distribution of Impressions From Explore")
sns.distplot(data['From Explore'])
plt.show()

C:\Users\Rakesh\Downloads\Anaconda\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
In [22]:
home = data['From Home'].sum()
hashtags = data['From Hashtags'].sum()
explore = data['From Explore'].sum()
other = data['From Other'].sum()

labels = ['From Home', 'From Hashtags', 'From Explore', 'Other']
values = [home, hashtags, explore, other]
fig = px.pie(data,values=values, names=labels,
            title='Impressions on Instagram Posts From Various Sources', hole=0.5)
fig.show()
In [23]:
text = " ".join(i for i in data.Caption)
stopwords = set(STOPWORDS)
wordcloud=WordCloud(stopwords=stopwords, background_color='white').generate(text)
plt.style.use('classic')
plt.figure(figsize=(12,10))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show()
In [24]:
text = " ".join(i for i in data.Hashtags)
stopwords = set(STOPWORDS)
wordcloud = WordCloud(stopwords=stopwords, background_color="white").generate(text)
plt.style.use('classic')
plt.figure( figsize=(12,10))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.show()

Relationship Analysis¶

In [25]:
figure = px.scatter(data_frame = data, x='Impressions', y='Likes', size='Likes', trendline='ols', title = "Relationship Between Likes and Impressions")
figure.show()
In [26]:
figure = px.scatter(data_frame = data, x="Impressions",
                    y="Comments", size="Comments", trendline="ols", 
                    title = "Relationship Between Comments and Impressions")
figure.show()
In [27]:
figure = px.scatter(data_frame = data, x="Impressions",
                    y="Shares", size="Shares", trendline="ols", 
                    title = "Relationship Between Shares and Impressions")
figure.show()
In [28]:
figure = px.scatter(data_frame = data, x="Impressions",
                    y="Saves", size="Saves", trendline="ols", 
                    title = "Relationship Between Saves and Impressions")
figure.show()
In [29]:
conversion_rate = (data['Follows'].sum() / data['Profile Visits'].sum()) * 100
print(conversion_rate)

31.17770767613039
In [31]:
figure = px.scatter(data_frame = data, x="Profile Visits",
                    y="Follows", size="Follows", trendline="ols", 
                    title = "Relationship Between Saves and Impressions")
figure.show()
In [32]:
x = np.array(data[['Likes','Saves','Comments','Shares', 'Profile Visits','Follows']])
y = np.array(data['Impressions'])
xtrain, xtest, ytrain, ytest = train_test_split(x,y,test_size=0.2,random_state=42)
In [33]:
model = PassiveAggressiveRegressor()
model.fit(xtrain, ytrain)
model.score(xtest, ytest)
Out[33]:
0.9356229525007367
In [34]:
features = np.array([[282.0, 233.0,4.0, 9.0, 165.0, 54.0]])
model.predict(features)
Out[34]:
array([10689.78508263])